Analysis and Identification of Students with Financial Difficulties: A Behavioural Feature Perspective

Algorithm 1

The Apriori Balanced Algorithm (ABA).

	Input: The Dataset D, Balanced_support threshold value S
	Output: Maximum frequent k item set
(1)	Scan all the datasets and get all the data that have appeared, as a candidate frequent 1-item set.
(2)	k = 1, the frequent 0-item set is considered an empty set.
(3)	While 1 do:
(4)	Scan data to calculate the Balanced_support of candidate frequent k item set
(5)	Remove the datasets whose Balanced_support of candidate frequent k item set is lower than the threshold value S. Get frequent k items.
(6)	If The frequent k item set is Empty Then:
(7)	return frequent k − 1 item sets as result, and ABA over.
	End if
(8)	If the number of items in frequent k dataset is equal 1 Then:
	return frequent k item set as result, and ABA over.
	End if
(9)	k = k+1
(10)	End while